This directory contains code for our VAE model and neural network predictor based on the learned video representations from the VAE. 

1)vae_new_LSTM.py contains the main model with LSTM layers for the sequential list of words in the text modality and VGG19 features of the sequential video frames in the image modality

2)featuresweights.py generates the latent video representations based on the weights learned from the VAE model for both train & test sets. 

2)nn_predictor.py contains the neural network predictor that takes the video representations of the videos in the train set and their growth labels/residuals as input and outputs the prediction accuracy, precision, recall and F1 score on the test set with 10 iterations. 

3)dtforvideo.py generates the date at which the video was posted and dttomatrix.py converts that into time-specific effect used in our prediction (the hashtag-specific effect was generated under the database/output/combine_traintest_ht_new.py)
